to be transferred from the environment to the learner. Based on the five criteria, a six‐level learning taxonomy was proposed. The taxonomy considers two extremes: no active learner participation and complete active learner participation. Examining the taxonomy, one can easily see the influence of Socrates.
1 Rote learning – A memorization process that requires little thought of meaning by the learner.
2 Learning with a teacher – Most of the information is provided by the teacher. Missing details must be inferred by the learner.
3 Learning by example – Specific conceptual instances are given; however, generalization must be achieved by the learner.
4 Learning by analogy or metaphor – Related conceptual instances are given. The learner must recognize the relation and apply it to the task at hand.
5 Learning by problem solving – Knowledge embedded in the problem may be gained by the learner through solving the problem.
6 Learning by discovery – Knowledge exists but must be hypothesized by the learner through theory formation and extracted by experiment.
1.4.1 Rote Learning
Habit or rote learning is the simplest learning process. The environment supplies all of the knowledge, and the learner merely accepts and stores it with no thought to meaning or content. Despite its elementary nature, the rote acquisition of knowledge is essential to all higher forms of learning. The learner must retain base information to be able to apply it to future problems.
B.F. Skinner took a somewhat different view. He suggested that no clear connection had been demonstrated in education between ends and means. He contended that the educational process should be reduced to defining goals or acts that the learner was able to perform. Based on the “present” state of knowledge, a sequence of acts could be created to move the learner from the present to the desired state. Often, the teacher would not be necessary.
One of the most familiar and perhaps best early instances of mechanized rote learning is Arthur Samuel's program designed to play the game of checkers. The program was initially equipped with a number of suggested procedures for playing the game correctly. The intent was to have the program learn by memorizing successful (deemed significant) board positions as it encountered them and then to use them properly and effectively in future games. Ultimately, the program progressed to the level of skilled novice.
1.4.2 Learning with a Teacher
Learning with a teacher is the first level of increased complexity in the learning hierarchy. Here, the learner is beginning to take an active role in several phases of the process, specifically she or he may request information from the teacher. In this situation, abstracted or general information is presented in an integrated manner by the teacher. The learner must accept the information and then complete the store of knowledge by inferring the missing details.
Many successful programs have been written using such a paradigm. In these, the program played the role of the student or learner. Several programs, including Mostow's FOO program for playing the card game “hearts” and Waterman's poker player, were oriented toward game playing. Davis's TEIRESIAS program presented an interesting variation on this scheme.
Rather than being autonomous, TEIRESIAS was designed to sit in front of the MYCIN program written earlier by E.H. Shortliffe. MYCIN was a large rule‐based system designed to assist physicians in the diagnosis and therapy of infectious diseases. The design and development process for any such large‐scale system is both iterative and refining. If the system makes a misdiagnosis or offers advice contrary to the physician's diagnosis, the knowledge base must be modified. Under such a condition, TEIRESIAS would interact with the user to correct the difficulty. Such a situation reduces to a two‐part task: first, explaining to the user the line of reasoning that led to the conclusion and then second, asking what additional or different information is needed to alter the result.
1.4.3 Learning by Example
Learning by example or induction increases the level of participation by the learner in the learning process. Unlike the previous example in which the teacher abstracted and then presented the material, here the student must assume the responsibility for the task. In such a context, specific conceptual instances are presented, and the student must recognize the significant or key features of the examples and then form the desired generalizations.
An early classic example of such an approach is Patrick Winston's work on “Learning Structural Descriptions from Examples.” The goal of Winston's program was to learn elementary geometrical constructs such as those one might build using toy blocks. The program was presented with training instances from which it evolved an internal description of the concept it was to be learning. The knowledge acquired was incorporated into a semantic network where all of the interrelationships among the constituent elements were described.
Critical to the effective use of Winston's algorithm are the ideas that positive training instances are evolutionary rather than revolutionary. In Winston's algorithm, negative training instances are those that reflect only minimal differences from the concept being investigated; thus, no learning occurs.
1.4.4 Analogical or Metaphorical Learning
Analogical or metaphorical learning is probably one of the more common methods by which human beings acquire new knowledge. As Winston points out in his work Learning and Reasoning by Analogy, with such an approach, once again, the learner's contribution to the process is increased. When learning by example, the learner is presented with positive and negative instances of the concept to be learned. With an analogy, the student has only closely related instances from which to extract the desired concept.
Jaime Carbonell identifies transformational and derivational as the two principal methods of reasoning by analogy. When learning by transformational analogy, the line of reasoning proceeds incrementally from some old or known solution to the new or desired solution through a series of mappings means‐ends called transform operators. The operators are applied using a means‐ends paradigm until the desired transformation is achieved.
Knowledge acquisition by derivational analogy achieves learning by recreating the line of reasoning that resulted in the solution to the problem. The reconstruction includes both decision sequences and attendant justifications.
1.4.5 Learning by Problem Solving
Learning by problem solving can easily be viewed as subsuming all other forms of learning discussed. However, such a technique has sufficient merit in its own right that it deserves individual consideration. With this approach, the knowledge to be imparted is embedded in a problem or sequence of problems. The objective is for the learner to acquire that knowledge by solving the problem. The most serious difficulty with such an approach is the intolerance of individual method.
Consider the question: “What is the sum of ¼ and ¼?” Although a response of 2/4 would be completely correct, that answer may be considered wrong since it did not match the “correct” answer of ½.
1.4.6 Learning by Discovery
Learning by discovery is the antithesis of rote learning. In this paradigm, the learner is the initiator in all five phases of learning discussed earlier. There is no new knowledge in the world since all knowledge already exists and is merely waiting some clever individual to discover it.
According to Carbonell, two basic methods of acquiring knowledge by discovery are available: observation and experimentation. Observation is considered to be a passive approach because the learner collects information by watching a particular event and then later forms a theory to explain the phenomenon. In contrast, experimentation is viewed as active. Here, the process generally involves the learner postulating a new theory about the existence